Purpose

Here I am doing a small experiment to compare different genetic architectures between snakes and newts. I will use the cost_1on1 simulation and my snake_newt_gv msprime simulation.

Outline

-Pick 4 different mu sigma combinations where mu*sigma^2 is constant (mu=1e-11, sigma=5) to (mu=1e-8, sigma=0.005) i.e ((mu=1e-11, sigma=5(A)), (mu=1e-10, sigma=0.5(B)), (mu=1e-9, sigma=0.05(C)), (mu=1e-8, sigma=0.005(D))) The ABCD represents the group of mu and sigma. When in the newt and snake simulation the groups will be paired to make 16 pairs. -In total that will be 16 msprime simulations and 16 slim simulations -See if there are any obvious winners -make a co-evolutionary heatmap

I ran msprime simulations and the slim simulations on cluster in GA_lt folder. Mt goal is to first see if there is an obvious “winner” in the co-evolutionary arms race between these species.

First, I want to look at the mean pheotypes of newts and snakes as the generations increase. Then I want to look at the difference between the mean phenotypes for each of the 16 simulations. I need to make the data frame to compare all 16 simulations. I noticed in the mu=1e-11, sigma=5 simulations that the staring pheotypes of some of the individulas were really high and sometimes the population crashed and died out.

## Warning: Removed 3 row(s) containing missing values (geom_path).

## Warning: Removed 3 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

First things that I noticed it that there are some newt and snake phenotypes that start of really high

lets look at other things like population size and newt deaths

## Using snake, newt as id variables

## Using snake, newt as id variables